1. Field of the Invention
The present invention is directed to an adaptive predictive expert control system for controlling single-input single-output, or multivariable time-variant processes, with known or unknown parameters and with or without time delay. More particularly, the present invention is directed to a system with an expert block controlling previously known adaptive-predictive control systems. This expert block operates using rules that can determine and/or modify the operation of a driver block, a control block and an adaptive mechanism. The expert block can accommodate evolution of the process input/output (I/O) variables used with the above blocks and mechanisms. Application of the expert block to control systems defined in previous adaptive-predictive methodologies improves performance, robustness and stability of the overall system.
2. Description of the Related Prior Art
The application of adaptive-predictive control systems using adaptive-predictive controllers is well known. Such adaptive-predictive control systems are described in European Patent No. 0037579, issued Aug. 20, 1986, entitled xe2x80x9cAdaptive-Predictive Control Method and Adaptive-Predictive Control Systemxe2x80x9d, U.S. Pat. No. 4,358,822 issued Nov. 9, 1982, entitled xe2x80x9cAdaptive-Predictive Control Systemxe2x80x9d, which is hereby incorporated by reference; U.S. Pat. No. 4,197,576, issued Apr. 8, 1980, entitled xe2x80x9cAdaptive-Predictive Control Systemxe2x80x9d; and United Kingdom Patent No. 1583545, dated Jul. 1, 1977 and entitled xe2x80x9cImprovements In an Relating to Control Systemsxe2x80x9d, all issued to the present applicant.
The adaptive-predictive controllers are operable to predict, by means of an adaptive-predictive (AP) model included in the control block, the value of a set of dynamic process output variables. The set of dynamic process output variables form a dynamic process output vector at a future sampling instant. The adaptive-predictive controller also generate at each sampling instant, using the AP model, a predicted control vector that causes the predicted dynamic process output vector to equal a desired dynamic process output vector at the future sampling instant. The desired dynamic process output vector is generated by a driver block according to desired performance criterion.
In addition, adaptive-predictive controllers include an adaptive mechanism that periodically updates the parameters of the AP model within the control block. The updates occur in such a way that the difference between the actual value of the dynamic process output vector at the future sampling instant and the value of the dynamic process output vector predicted by the control block is reduced towards zero.
Adaptive-predictive control systems have proven reliability and excellent performance when applied to industrial processes. However, their performance, robustness and stability become less reliable when the controlled process is very non-linear, is time varying and/or evolves in the presence of strong noises and perturbations. In these situations, it must be determined when the adaptive predictive control can be applied successfully, and when it may be advantageous to use the available real time process information to model the input/output relationship of the process. Thus, a new control solution is desired wherein:
a) The experience in the application of adaptive-predictive control could be used (i) to develop rules to determine in real time when adaptive-predictive control is advisable; and (ii) when adaptive-predictive control is advisable, to develop additional rules to determine how it must be applied and when adaptation of the AP model parameters must be performed.
b) When adaptive-predictive control is not advisable, the experimental knowledge of the human operator should be taken in to account by a further set of rules that will apply an xe2x80x9cintelligentxe2x80x9d control vector to the process.
The present invention is an improvement over the previously disclosed adaptive-predictive control systems, as indicated in the above mentioned U.S. Pat. Nos. 4,358,822 and 4,197,576, and United Kingdom Patent No. 1583545.
The adaptive-predictive expert control system of the present invention adds an expert block into the operation of previously known adaptive-predictive control systems. The expert control determines and/or modifies the operation of the driver block, the control block and the adaptive mechanism of the previous art. The expert block operates with different sets of rules, for example:
a) A first set of rules which can determine whether or not the control block can use the AP model to generate a control vector by applying adaptive-predictive control as defined by the previous art.
b) When the AP model can be used to generate the control vector, a second set of rules can determine whether or not the parameters of the AP model can be updated from the real time process I/O variables measurements.
c) When the AP model can be used to generate the control vector, a third set of rules can determine whether or not control limits applied to the predicted control vector must be reduced appropriately.
d) When the AP model should not be used to generate the control vector, the control block will use a fourth set of rules, based on the human operator control experience, to generate the control vector to be applied to the process.
e) When the AP model can be used to generate the control vector, a fifth set of rules can determine whether or not the performance criterion of the driver block must be redefined.
f) When the AP model can be used to generate the control vector, a sixth set of rules can determine whether or not the parameters of the AP model must be reinitialized to some predefined values.
The above considered sets of rules, within the expert block, imitate in different ways human intelligence. For instance, these rules can take into account specific domains in which the process I/O variables may reside and the length of xe2x80x9ctime of residencexe2x80x9d, understanding by xe2x80x9ctime of residencexe2x80x9d the number of consecutive control periods that the process I/O variables remain in a specific domain.
The relation between the sets of rules and the specific domains and times of residence may be defined, for instance, as follows:
1) The first set of rules may examine a first domain and a first time of residence for a dynamic process output vector, containing at least one process output variable. The first set of rules can then determine that adaptive-predictive control will be applied when the dynamic process output vector resides in the first domain for a period of time in excess of the predetermined first time of residence.
2) The second set of rules may examine a second domain and a second time of residence for the dynamic process output vector. The second set of rules can determine that adaptation by updating the AP model parameters should be stopped while adaptive-predictive control is applied. The updates to the AP model can be halted when the dynamic process output vector resides in the second domain for a period of time in excess of the second time of residence.
3) The third set of rules may examine a third domain and a third time of residence for the dynamic process output vector. The third set of rules can carry out an appropriate tightening of control limits while adaptive-predictive control is applied. The tightening of control limits is applied when the dynamic process output vector resides in the third domain for a period of time in excess of the third time of residence.
In addition, while the dynamic process output vector is in the domain for adaptive-predictive control, the expert block will always be able to modify the control block parameters and/or to redefine the driver block performance criterion taking into account the particular operating conditions of the process and the desired performance of the control system. Thus:
4) The fifth set of rules may examine the dynamic process output vector evolution to redefine the driver block performance criterion according to a desired control system performance. A first set of subdomains related to the domain for application of adaptive-predictive control is defined according to a desired control system performance criteria. The redefinition of the driver block performance criterion can occur when the dynamic output process vector enters and resides in a subdomain of the first set of subdomains for a predetermined time of residence.
5) The sixth set of rules may examine the dynamic process output vector evolution to determine when the parameters of the AP model should be partially or totally reinitialized. The reinitialization can use the experimental knowledge available for the process dynamics in a subdomain of a second set of subdomains. Again, the second set of subdomains is related to the domain for application of the adaptive-predictive control. The reinitialization can be applied when the dynamic process output vector resides in a subdomain of the second set of subdomains for a predetermined time of residence.
The distinctive feature of the present invention allows the adaptive-predictive expert control system to control processes submitted to the influence of noises and perturbations and/or with a highly non-linear dynamic and time varying nature in such a way that the performance, robustness and stability of the new system is significantly better than that of the previous art, as explained in the following.
As previously discussed, the functioning of the expert block allows to combine adaptive-predictive control with rule based control. The first one will be used in the domain of operation where it is possible to describe satisfactorily the process dynamic behavior by means of an AP model and, thus, achieve precise, high performance control, as described in the previous art, and the second one in the domain of operation where it is advisable to use the experience of the human operator and emulate his behavior by means of rules. As a matter of fact, inmost industrial applications of adaptive-predictive controllers, they are on automatic mode only in a certain domain of operation and, when the process goes away from this domain, the operator takes manual control over the plant. Thus, this distinctive feature of this invention allows to integrate the operators knowledge within the automatic control system, increasing in this way its robustness, autonomy and stability in the overall operation of the plant.
Also another distinctive feature of the present invention allows to stop the operation of the adaptive mechanism when the process should not apply adaptive-predictive control or when the process I/O variables attain a certain domain, in which the effect of noises and perturbations may cause an undesirable drift on the AP model parameters within the control block. For instance, this may happen when the process approaches stability and, in this situation, the level of noises and perturbations is relatively large in relation with the variation on the control vector. Then, it is advisable to disregard the process I/O variables information for estimation purposes and, therefore, to stop the updating of the AP model parameters. Otherwise the above mentioned drift in the AP model parameters originates unstability in the operation of the control system.
In a similar way an additional feature of the present invention allows to avoid the problem of large erratic predictive control actions that may for instance be originated when adaptive-predictive control tries to compensate for stochastic noises and perturbations, added on the dynamic process output vector, and the level of said stochastic noises and perturbations is relatively high in relation with the actual overall tendency on the evolution of the dynamic process output vector. The definition of a domain for limited control solves this problem providing a smooth and efficient control action, in spite of the stochastic noises that may act on the process, and avoiding high frequency large excursions on the control vector.
A further additional feature of the present invention allows to reinitialize totally or partially the parameters of the AP model within the control block when the dynamic process output vector under adaptive-predictive control evolves attaining subdomains of operation where important, even discontinuous, changes in the process dynamics may occur, as happens in a PH process or in a high performance aircraft. This further additional feature of the present invention uses the knowledge available about the process dynamics on said subdomains of operation to avoid the significant deterioration that said changes in the process dynamics may cause in the performance of the control system.
Another feature of the present invention allows modification of the driver block performance criterion to accommodate the desired performance of the control system to the operating conditions when required, thus improving the overall performance of the system.
In the following detailed description of the invention, the adaptive-predictive expert control system is presented as an extension of the previous adaptive-predictive control system disclosed in the U.S. Pat. No. 4,197,576, in order to allow a better and simple understanding of the invention, but also includes features of other disclosures of the previous art, such as those of the European Patent No. 0037579 and the U.S. Pat. No. 4,358,822. For instance, in the U.S. Pat. No. 4,197,576 the prediction horizon used is equal to 1, but the invention may be directly applied just as easily to an embodiment with a larger prediction horizon, as that considered in European Patent No. 0037578 and U.S. Pat. No. 4,358,822. It is important to emphasize that the invention is an extension of the previous art on adaptive-predictive control and therefore may be applied as an extension of any of the embodiments of said previous art.